Bibliographic record
Abstract
Experts trace a congruent trend, pinpointed as originating around the 2010s (Grose, 2020) and only accelerating in the pandemic and its aftermaths: the rise of social media activity relating to parents’ performances of their substance abuse – what this paper defines as “#winemom culture” – with a broader social tendency, a general increase in “rates of high-risk drinking” that lead to such outcomes as “long-term health damage” and “dangers to family” (Macarthur, n.d.). I interrogate the ethics of moralizing against #winemom culture under COVID-19 culture and its aftermaths through exclusively quantitative metrics or surface-level analysis. As with anything coded according to the “momification of the Internet” (Dewey, 2015), such cultures are often disregarded, seen as superficial or in receipt of unchecked judgments. I trace the following question: What can #winemom culture reveal about how parents are processing and communicating within this moment? And begin from the premise that there are as-yet undetermined drivers motivating what appears to be a “zoning out” (Heyes, 2020) in the mediation of #winemom culture production. This project then opens into an analysis of how to actually study digital feminist practices in this current moment, one that is defined by methodological crises surrounding the increasing complexities of enacting justice in social media research. This paper thus serves as a methodological disquisition for feminist researchers attempting to perform ethically just social media research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".